Characterization of clear cell renal cell carcinoma by gene expression profiling

Urologic Oncology: Seminars and Original Investigations ] (2015) ∎∎∎–∎∎∎

Original article

Characterization of clear cell renal cell carcinoma by gene expression profiling Bryan J. Thibodeau, Ph.D.a,*, Matthew Fulton, M.D.b, Laura E. Fortier, M.S.a, Timothy J. Geddes, B.S.a, Barbara L. Pruetz, B.S.a, Samreen Ahmed, M.P.H.a, Amy Banes-Berceli, Ph.D.d, Ping L. Zhang, M.D., Ph.D.c, George D. Wilson, Ph.D.a, Jason Hafron, M.D.b a Beaumont BioBank, Beaumont Health System, Royal Oak, MI Department of Urology, Beaumont Health System, Royal Oak, MI c Department of Anatomic Pathology; Beaumont Health System, Royal Oak, MI d Oakland University, Rochester, MI b

Received 26 June 2015; received in revised form 27 October 2015; accepted 2 November 2015

Abstract Objectives: Use global gene expression to characterize differences between high-grade and low-grade clear cell renal cell carcinoma (ccRCC) compared with normal and benign renal tissue. Methods: Tissue samples were collected from patients undergoing surgical resection for ccRCC. Affymetrix gene expression arrays were used to examine global gene expression patterns in high- (n ¼ 16) and low-grade ccRCC (n ¼ 13) as well as in samples from normal kidney (n ¼ 14) and benign kidney disease (n ¼ 6). Differential gene expression was determined by analysis of variance with a false discovery rate of 1% and a 2-fold cutoff. Results: Comparing high-grade ccRCC with each of normal and benign kidney resulted in 1,833 and 2,208 differentially expressed genes, respectively. Of these, 930 were differentially expressed in both comparisons. In order to identify genes most related to progression of ccRCC, these differentially expressed genes were filtered to identify genes that showed a pattern of expression with a magnitude of change greater in high-grade ccRCC in the comparison to low-grade ccRCC. This resulted in the identification of genes such as TMEM45A, ceruloplasmin, and E-cadherin that were involved in cell processes of cell differentiation and response to hypoxia. Additionally changes in HIF1α and TNF signaling are highly represented by changes between high- and low-grade ccRCC. Conclusions: Gene expression differences between high-grade and low-grade ccRCC may prove to be valuable biomarkers for advanced ccRCC. In addition, altered signaling between grades of ccRCC may provide important insight into the biology driving the progression of ccRCC and potential targets for therapy. r 2015 Elsevier Inc. All rights reserved.

Keywords: Clear cell renal cell carcinoma; Microarray; Gene expression; Fuhrman grading

1. Introduction Clear cell renal cell carcinoma (ccRCC) is the most common form of renal cancer, accounting for 75% to 85% of renal tumors. Kidney cancer is among the 10 most common cancers in both men and women with approximately 61,560 new cases and 14,080 deaths from this Corresponding author. Tel.: þ1-248-551-0275; fax: þ1-248-551-2443. E-mail address: [email protected] (B.J. Thibodeau). *

http://dx.doi.org/10.1016/j.urolonc.2015.11.001 1078-1439/r 2015 Elsevier Inc. All rights reserved.

disease expected in 2015 [1]. With the escalation of abdominal imaging, incidental findings have resulted in an increased incidence of small renal masses. Although many of these are benign, some prove to be malignant and require further action. Whereas small renal masses (r4 cm) tend to have low pathological stage, a certain percentage are locally advanced [2], and tumor size does not differ between localized and metastatic disease [3]. Fuhrman grading has long been used to assess prognosis of ccRCC [4] and is defined based on increasing nuclear

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size, irregularity, and nucleolar prominence. Fuhrman grading is closely associated with patient survival [5]. Patients with grades 1 and 2 RCC showed a 94% and 86% 5-year survival rate, respectively, whereas grades 3 and 4 RCC demonstrated survival rates of only 59% and 31% [6]. High Fuhrman grading has been correlated with increased expression of many individual markers, including colony stimulating factor 1 [7], transglutaminase 2 [8], and p21-activated kinase 4 (PAK4) [9], but an exploration has not been made based on global gene expression patterns. Although the diagnosis of RCC in most cases is established by nephrectomy or partial nephrectomy to obtain tissue for histology, there have been other efforts to investigate molecular biomarkers for diagnosis and prognosis in ccRCC. Genetic changes in the von HippelLindau (VHL) gene have long been associated with RCC [10,11]; however, other individual markers have also been identified. High-expression levels of interleukin-1β and interleukin 18 are predictive of poor prognosis [12]. Elevated expression of hypoxia-inducible protein (HIF) 2, HIF1α, and phosphorylated nuclear factor-kappa β have been shown to be involved in the progression of kidney cysts through tumor development [13]. Others have implicated activation of certain pathways. The mammalian target of rapamycin pathway was shown to be prognostic for recurrence in RCC by examining PTEN, phosphorylated AKT, phosphorylated mammalian target of rapamycin, phosphorylated p70 ribosomal S6 kinase, and phosphorylated 4E-binding protein 1 [14]. Data from the Cancer Genome Atlas have also elucidated potential molecular prognostic signatures using discovery and validation data sets [15]. Aggressive RCC demonstrated a consistent pattern of altered cellular metabolism, which manifested as a metabolic shift towards fatty acid synthesis (i.e., Warburg-like phenotype). The PI(3)K-AKT pathway was found to be repeatedly altered including mutations in genes such as GNB2L1 and SQSTM1 that are involved in activation of signaling. In addition, there is deregulation of the AKT pathway through altered methylation resulting in differential mRNA expression (e.g., hypomethylation of the microRNA 21 promoter and hypermethylation of the PI(3)K inhibitor GRB10). Although other studies aimed to identify markers of prognosis, we utilized gene expression microarrays to examine high- (Fuhrman grades 3 and 4) and low(Fuhrman grades 1 and 2) grade ccRCC as well as normal and benign kidney tissue in order to characterize the differences between high-grade and low-grade ccRCC. This enabled the discovery of gene expression biomarkers that can differentiate ccRCC from normal and benign tissue and differentiate high-grade and low-grade ccRCC. In addition to individual targets, larger signaling pathways are also documented as crucial to the progression of ccRCC.

2. Materials and methods 2.1. Sample acquisition Patients of JH undergoing surgical resection at William Beaumont Hospital gave consent to clinical staff prior to surgery as approved by the local Human Investigation Committee. Tissue samples were collected by the Beaumont BioBank, a College of American Pathologists (CAP)accredited biorepository that follows standard operating procedures for collection and storage of tissue specimens. Tissue samples were collected in the operating room and entered into permanent storage with a median time of 38 minutes. Fuhrman grading was obtained from the patient‘s pathological report. 2.2. Ribonucleic acid isolation Ribonucleic acid (RNA) was isolated from fresh frozen tissue using E.Z.N.A. Total RNA Kit I (Omega, Norcross, GA) including deoxyribonuclease treatment. Tissue was homogenized using the gentleMACS dissociator (Miltenyi Biotec, Auburn, CA) according manufacturer's protocol. RNA quality was determined on an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA). 2.3. Affymetrix gene expression Total RNA from each sample was labeled using the WT Expression Kit (Ambion, Austin, TX). Fragmentation and labeling were performed according to the Affymetrix GeneChip WT Terminal Labeling Kit protocol. GeneChip Human Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA) were hybridized overnight, washed and stained on a GeneChip Fluidics Station 450 in accordance with the GeneChip Hybridization, Wash, and Stain Kit protocol and then scanned with the GeneChip Scanner 3000 7G. 2.4. Differential gene and pathway analysis Analysis was done using Partek Genomics Suite (Partek Inc., St. Louis, MO; version 10.6.5.0). Normalization was done using a Robust Multichip Average algorithm, which includes a background correction, quantile normalization, log probes base 2, and median polish probeset summarization. Differential genes were identified using analysis of variance. Pathway Studio (version 11.0, Elsevier, Atlanta, GA) was used for sub-network and pathway analysis. The data are available using NCBI Gene Expression Omnibus accession number GSE68417 [16]. 2.5. Quantitative real-time polymerase chain reaction (qRT-PCR) The following pre-designed TaqMan gene specific primers (Life Technologies, Carlsbad, CA) were used: ACTB

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(Assay ID: Hs99999903_m1), AGPAT9 (Assay ID Hs00262010_m1), CP (Assay ID: Hs00236810_m1), HK2 (Assay ID: Hs00606086_m1), lysyl oxide (LOX) (Hs00942480_m1), and TMEM45A (Hs01046616_m1). Quantitative real-time PCR reaction mixture was prepared containing 2 ml cDNA (10 ng), 1x TaqMan Gene Expression Master Mix (Life Technologies, Carlsbad, CA), and 1x Gene Expression Assay (Life Technologies, Carlsbad, CA). Gene expression levels were quantified using the ViiA 7 Real-Time PCR system (Life Technologies, Carlsbad, CA). The following thermocycling condition was used: 501C for 2 minutes, 951C for 10 minutes, and 40 amplification cycles of 951C for 15 seconds/601C for 1 minute. All samples were performed in triplicate. The delta cycle threshold method was used for analysis.

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Table 1 describes the patient and lesion characteristics for the samples used for the current study. Histologic grades 1 and 2 were grouped into low-grade ccRCC (n ¼ 13), whereas histologic grades 3 and 4 composed the high-grade ccRCC cohort (n ¼ 16). Nonmalignant tissue samples were comprised of normal tissue adjacent to tumor (n ¼ 14) and benign conditions (n ¼ 6). Each group had similar gender ratios and similar average age.

low grade and benign, and 2,208 genes between high-grade and benign. To highlight changes essential to progression, analysis was focused on genes that are differentially expressed in high-grade ccRCC (Fig. 1). A total of 930 genes are differentially expressed in high-grade ccRCC compared with both normal kidney and benign disease (Supplemental Table S1). Genes were filtered to show a trend from normal or benign to low-grade to high-grade ccRCC. For example, for genes overexpressed in ccRCC (positive-fold change), Fold changehigh-grade ccRCC 4 Fold changelow-grade ccRCC 4 Fold changenormal/benign. Table 2 shows the 20 genes with the greatest magnitude of change between the high-grade ccRCC comparisons and low-grade comparisons. Genes such as transmembrane protein 45A, ceruloplasmin (CP), and aquaporin 9 demonstrated the greatest levels of up-regulation in high-grade ccRCC compared to normal and benign whereas cadherin 1 and tetraspanin 7 were more down-regulated in the highgrade ccRCC. Samples from 20 patients (6 normal kidney tissue, 3 benign kidney disease, 5 low-grade ccRCC, and 6 highgrade ccRCC) were used to validate the microarray results. Reverse transcription polymerase chain reaction (RT-PCR) confirmed the microarray results of all five genes examined (Table 3). As seen previously [17], results from microarray analysis tend to underestimate the degree of change found by RT-PCR; nevertheless, TMEM45A, LOX, HK2, CP, and AGPAT9 show the same pattern of expression by both microarray and RT-PCR.

3.2. Gene expression differences

3.3. Altered regulation of cell signaling

Gene expression analysis initially focused on a direct comparison of high-grade ccRCC to low-grade ccRCC. Only a single gene reached statistical significance (false discovery rate of 5%) between the 2 groups: transmembrane protein 45A (TMEM45A), which is up-regulated 4.6-fold in high-grade ccRCC. In order to further characterize ccRCC, 4 additional comparisons were executed: low grade vs. normal, high grade vs. normal, low grade vs. benign, and high grade vs. benign. A large number of genes were differentially expressed (FDR of 1% and 2-fold cutoff) in each comparison: 1,642 genes between low-grade and normal, 1,833 genes between high-grade and normal, 2,117 genes between

For signaling analysis, the focus was expanded to include the 143 genes that demonstrated fold changes that were one-third or greater in the high-grade ccRCC comparisons vs. the comparisons to low-grade ccRCC (Supplemental Table S2). Pathway Studio constructs sub-networks of genes that are involved in regulating cellular processes. A Fisher's exact test was used to identify sub-networks that are highly represented in the list of 143 genes. Included among the top sub-networks (Table 4) are cell survival, cell differentiation, and response to hypoxia. Sub-networks of genes with expression that is controlled by a single seed entity were also examined. Table 5 lists the top expression target sub-networks. The seeds for these 10 sub-networks

3. Results 3.1. Patient demographics

Table 1 Clinical data

Normal Benign Low-grade ccRCC High-grade ccRCC

n

Histologic grade or diagnosis

Age, average (range)

Gender

Mets

14 6 13 16

Adjacent to low-grade (n ¼ 7) or high-grade ccRCC (n ¼ 7) Non-functional kidney (n ¼ 1), oncocytoma (n ¼ 4), renal cyst (n ¼ 1) Grade 1 (n ¼ 3), Grade 2 (n ¼ 10) Grade 3 (n ¼ 14), Grade 4 (n ¼ 2)

61.4 57.5 61.1 60.1

5F, 5F, 6F, 6F,

2 Yes, 12 no 6 No 13 No 3 Yes, 13 no

(48-78) (21-77) (30-87) (48-89)

9M 1M 7M 10M

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Fig. 1. Venn diagram illustrating the number of differentially expressed genes (FDR 1% and 2-fold cutoff). Area in gray highlights the 930 genes differentially expressed in the comparison of high-grade ccRCC compared with both normal kidney tissue and benign kidney disease. (Color version of figure is available online.)

were subsequently investigated for their association with RCC and, in particular, clear cell carcinoma. Fig. 2 illustrates the links between each of the entities and RCC/ ccRCC. Of the 10 expression target seeds, 9 were associated with RCC with 6 being linked to ccRCC. Only 1 entity, SERPINF1, was not directly linked to either.

4. Discussion In earlier work by our group [18] and others [19], increased expression of the individual marker KIM1 (kidney injury molecule 1) was shown to differentiate RCC from nonmalignant kidney. Although that result was confirmed in

Table 2 Differentially expressed genes (FDR 1% and 2-fold cutoff) in high-grade ccRCC compared with normal and benign that exhibit the greatest difference to lowgrade ccRCC. Percentage change indicates the difference between the high and low-grade ccRCC fold changes. Fold changes in italics are negative; fold changes in bold are P r 0.01 Gene Symbol TMEM45A CP AQP9 MAP7D2 LOX DNAH11 FABP6 CDH1

Gene name

Transmembrane protein 45A Ceruloplasmin (ferroxidase) Aquaporin 9 MAP7 domain containing 2 Lysyl oxidase Dynein, axonemal, heavy chain 11 Fatty acid binding protein 6, ileal Cadherin 1, type 1, E-cadherin Hypothetical LOC100128252 SLC11A1 Solute carrier family 11 (proton-coupled divalent metal ion transporter), member 1 PPP1R3C Protein phosphatase 1, regulatory (inhibitor) subunit 3C NPTX2 Neuronal pentraxin II CXCL1 Chemokine (C-X-C motif) ligand 1 C5orf46 Chromosome 5 open reading frame 46 SLC39A14 Solute carrier family 39 (zinc transporter), member 14 HK2 Hexokinase 2 SLC16A6 Solute carrier family 16, member 6 TSPAN7 Tetraspanin 7 AGPAT9 1-Acylglycerol-3-phosphate O-acyltransferase 9 AHNAK2 AHNAK nucleoprotein 2

% Low-grade vs. Change normal

High-grade vs. Low-grade vs. High-grade vs. normal benign benign

463 450 356 344 299 287 217 216 212 210

2.98 10.14 1.71 (1.08) 4.79 7.23 3.21 (1.57) (1.89) 1.92

13.78 45.57 6.07 3.20 14.31 20.72 6.97 (3.39) (4.02) 4.04

2.21 22.76 2.22 1.77 14.21 7.75 1.66 (1.98) (1.93) 2.18

10.21 102.29 7.89 6.10 42.48 22.23 3.61 (4.27) (4.10) 4.57

197 194 193 193 192 192 188 186 185 184

3.84 3.10 1.33 1.62 1.62 5.99 1.77 (1.70) (8.36) 6.09

7.56 6.02 2.56 3.12 3.11 11.51 3.32 (3.15) (15.46) 11.23

2.43 2.91 1.88 1.55 3.16 5.71 1.65 (4.10) (10.26) 4.33

4.78 5.66 3.62 2.99 6.08 10.97 3.09 (7.62) (18.96) 7.99

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Table 3 RT-PCR validation of microarray results. Fold changes in italics are negative Microarray

RT-PCR

Low vs. normal High vs. normal Low vs. benign High vs. benign Low vs. normal High vs. normal Low vs. benign High vs. benign TMEM45A LOX HK2 CP AGPAT9

2.98* 4.79* 5.99* 10.14* (8.36)*

13.78* 14.31* 11.51* 45.57* (15.46)*

2.21* 14.21* 5.71* 22.76* (10.26)*

10.21* 42.48* 10.97* 102.29* (18.96)*

8.87*** 3.21*** 13.10* 26.34** (13.10)*

33.49* 29.06 28.70* 40.75* (16.30)*

9.73 18.50*** 86.60*** 255.70 (53.00)**

36.72* 167.50 189.90** 395.60* (65.90)*

r 0.05. r 0.10. *** P r 0.20. *P

** P

the current dataset (data not shown), here we expanded on that aim by using global gene expression analysis to characterize differences between high-grade and low-grade ccRCC. In order to achieve this objective, gene expression microarrays were performed on high-grade and low-grade ccRCC as well as non-tumorigenic conditions (normal kidney adjacent to tumor and benign kidney disease). We identified potential markers that demonstrate a progression from normal or benign through low-grade ccRCC to an extreme level of expression in the high-grade ccRCC. This included entities that have been previously shown to have a quantitative change in association with either RCC or ccRCC (Fig. 3). In the current study, CP is up-regulated more than 2fold in low-grade ccRCC compared with either normal kidney or benign, but is up-regulated more than 10-fold in high-grade ccRCC compared with either nonmalignant sample type. In the direct comparison, CP is up-regulated 4.5-fold (P ¼ 0.002) in high-grade vs. low-grade ccRCC. CP encodes a secreted metalloprotein that binds copper in plasma and has been shown in other studies to be upregulated in RCC [20–22]. Other genes are down-regulated in ccRCC. Cadherin 1 (CDH1, also known as E-cadherin) is down-regulated in both high-grade and low-grade ccRCC compared with normal and benign and is down-regulated 2.2-fold (P = 0.041) in high-grade vs. low-grade ccRCC.

The encoded protein is a cell-cell adhesion glycoprotein whose loss contributes to progression by increasing proliferation, invasion, or metastasis. In addition to confirming the current results of higher expression in ccRCC compared with normal and benign, CDH1 protein expression correlated to invasion and metastasis [23]. Although some genes had been previously implicated in ccRCC, other gene expression changes found in our data have been associated with other cancer types and cancer processes. Genes such as transmembrane protein 45A (TMEM45A) and aquaporin 9 (AQP9) have been associated with resistance to chemotherapy. TMEM45A shows a dramatic difference between high-grade and low-grade ccRCC with low-grade ccRCC up-regulated 2- to 3-fold compared to benign or normal, whereas high-grade levels are 10-fold greater than that of the nonmalignant tissue types. Although TMEM45A has not been extensively studied, it has been shown to be important in hypoxiainduced resistance to chemotherapy in in vitro experiments with breast and liver cancer cell lines [24]. Polymorphisms in AQP9 are associated with resistance to chemotherapy in lung cancer [25], and it is highly up-regulated in high-grade ccRCC compared with the normal and benign (3.6-fold, P ¼ 0.015). Other genes have previously been linked with tumor metastasis. LOX is a secreted copper enzyme that

Table 4 Sub-networks of genes regulating cell processes highly represented by the 143 genes that demonstrated a magnitude of change one-third or greater in the comparisons to high-grade ccRCC as compared with those seen to lowgrade ccRCC

Table 5 Expression target sub-networks highly represented by the 143 genes that demonstrated a magnitude of change one-third or greater in the comparisons to high-grade ccRCC as compared with those seen to low-grade ccRCC

Regulating cell processes of

Overlap

P value

Expression targets of

Overlap

P value

Cell survival Cell differentiation Response to hypoxia Cytoskeleton organization and biogenesis Innate immune response Cell migration Inflammatory response Cell adhesion Anchorage independent growth Phagocytosis

45 64 17 24 25 41 37 31 21 26

8.38E13 1.41E12 1.65E10 4.09E10 4.11E10 6.30E10 6.48E10 1.11E09 2.81E09 3.07E09

HIF1A TNF Histone deacetylase HIF-1 SERPINF1 IL6 PKC TGFB1 cytokine NF-kB

23 40 22 14 9 23 22 32 28 29

1.05E10 4.52E10 2.29E08 9.95E08 1.06E07 1.66E07 2.01E07 2.08E07 2.25E07 2.37E07

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Fig. 2. Pathway illustrating the known links between ccRCC and RCC to each of the top 10 expression target seeds. (Color version of figure is available online.)

initiates the crosslinking of collagens and elastin that has previously been shown to be up-regulated in ccRCC [22]. LOX is up-regulated in both the low-grade and high-grade ccRCC but shows nearly triple the level in high-grade

compared with low-grade ccRCC (3.0-fold, P ¼ 0.018). LOX expression has been identified as a prognostic indicator and a predictor for lymph node metastasis in oropharyngeal and esophageal cancer [26–28], whereas

Fig. 3. Subset of genes showing a pattern of expression with the fold change in high-grade carcinoma one-third greater than that seen in low-grade ccRCC that have been shown previously to have a quantitative change in ccRCC or RCC. Genes in red are up-regulated in ccRCC samples compared with normal and benign samples; blue, down-regulated. (Color version of figure is available online.)

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inhibition of LOX greatly decreased lung and liver metastasis in studies of breast cancer [29]. Chemokine C-X-C motif ligand 1 (CXCL1) is also involved in the metastatic potential of carcinoma. Seen here to be up-regulated in the high-grade ccRCC, it has previously been linked to the development and progression of hepatocellular carcinoma [30]; in breast cancer CXCL1 is associated with poor prognosis, increased detection of circulating tumor cells, and metastasis [31]. The sub-network analysis reveals categories that would be anticipated to be altered in a study of cancer progression; sub-networks of genes involved in regulating cell differentiation, cell survival, cell migration, and inflammatory response demonstrate differences between high-grade and low-grade ccRCC. In addition, expression target subnetworks implicate genes important in RCC. The importance of HIF1α (hypoxia-inducible factor 1α) signaling is an interesting case given controversy in the literature regarding the role of HIF1α in ccRCC. HIF1α has been associated with the loss of VHL tumor suppressor, which is a common occurrence in ccRCC. The loss of VHL leads to a stabilization of HIF1α, which allows the induction of gene expression associated with angiogenesis and cell survival [32]. However, other studies have shown that HIF1α inhibits the growth of RCC with much of the recent literature attempting to differentiate the effect of HIF1α and HIF2α on RCC [33–35]. Most recently Gudas et al. [36] presented that HIF1α is expressed in the majority of ccRCC and elevated levels of HIF1α correlates to poor prognosis. We saw no change in expression of either HIF1α or HIF2α, but HIF1α signaling was highly regulated between the high- and low-grade ccRCC. This may prove to be an interesting discovery as most previous studies compared RCC with normal kidneys. Here, the importance of HIF1α signaling is highlighted by separate comparisons of high-grade and low-grade ccRCC to nonmalignant kidney tissue. Furthermore, the gene expression patterns corroborate a study by Sanders and Diehl [37] that investigated the Warburg effect in RCC. Sanders and Diehl discovered that genes associated with glycolysis were overexpressed in ccRCC, whereas genes associated with gluconeogenesis were underexpressed. Our current study validated many of these results while expanding it to include high-grade ccRCC as well as comparisons with benign kidney conditions. For example, hexokinase 2 (HK2) is associated with glucose metabolism and enhanced aerobic glycolysis (the Warburg effect) in tumor cells, in particular in renal carcinogenesis [38]. It demonstrates a pattern of 5.5-fold up-regulation in low-grade ccRCC but 11-fold upregulation in high-grade ccRCC. Other genes that were identified by Sanders and Diehl as up-regulated in ccRCC include enolase 2 (ENO2), phosphofructokinase platelet, solute carrier family 2 member 3, pyruvate dehydrogenase kinase isozyme 1, and solute carrier family 16 member 1. In our study, all of these genes were up-regulated in ccRCC

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with ENO2, HK2, and PDK1 displaying increasing expression with higher grade ccRCC. Likewise, there was a strong concordance between our study and one that examined Wnt signaling in ccRCC [39]. Gumz et al. found that expression of secreted frizzledrelated protein 1 (sFRP1), a negative regulator of Wnt signaling, was lost in ccRCC compared with matched normal kidney; moreover, many Wnt-related genes were up-regulated in ccRCC. We found that SFRP1 was downregulated 34- and 21-fold in the high- and low-grade ccRCC compared with normal kidney. This corresponded with the increased expression in Wnt signaling pathway genes seen by Gumz et al: vascular endothelial growth factor, MYC, gap junction protein α1 (GJA1), fibronectin, vimentin, and TIMP metallopeptidase inhibitor 1 (TIMP1). Of these, both fibronectin and TIMP1 showed increasing expression with elevated histologic grade. In addition to sFRP1/Wnt signaling, genes that differentiate between high-grade and low-grade ccRCC were also highly represented by expression targets of TNF, a proinflammatory cytokine involved in the regulation of cell proliferation, differentiation, and apoptosis. Higher TNFα plasma levels were associated with poor survival in RCC patients [40]. In addition, many studies have examined the association of TNFα and RCC using in vitro studies. Zhang et al. [41] showed that treatment with TNFα resulted in a more mesenchymal phenotype and the expression of known stem cell markers. Several studies show that TNFα promoted invasion and migration and is associated with downregulation of E-cadherin [42–44]. Although the expression of TNFα was not altered in the current study, expression targets of TNFα signaling were differentially expressed between the ccRCC samples and the normal or benign samples. Additionally, these TNFα-associated genes were also different between the high-grade and low-grade ccRCC. In particular, the TNF-α target E-cadherin was down-regulated  4-fold in high-grade ccRCC compared with normal or benign kidney whereas it is down-regulated less than 2-fold in the low-grade ccRCC.

5. Conclusion The objective of this study was to determine gene expression markers to differentiate between high-grade and low-grade ccRCC. Furthermore, these markers should be able to distinguish ccRCC from normal and nonmalignant kidney tissue. To that end, microarray analysis identified a series of potential biomarkers that include gene expression of CP, hexokinase 2, and E-cadherin. In addition to these individual markers, sFRP1/Wnt, HIF1α, and TNF signaling are implicated as critical in the progression of ccRCC. Although further confirmation in larger cohorts is required, the current study's results provide potential biomarkers as well as insight into the biology of ccRCC progression.

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Acknowledgments Special thanks to Dianna Larson and the BioBank clinical staff for identifying and consenting potential patients. This work was supported by the generous support of the Beaumont Foundation Zafarana Fund.

Appendix A. Supplementary information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j. urolonc.2015.11.001.

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